Paper Citation Record · LEDGER

RewardAnything: Generalizable Principle-Following Reward Models

As of 15 August 2026, this Paper Citation Record lists 100 of 112 outbound references and 8 inbound Pith citation observations for arXiv:2506.03637.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.03637 v2

Coverage vector

measured 100 of 112 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:04:07.168303Z

measured 108 of 108 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T16:07:46.427272Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

100 of 112 outbound references displayed

verified exact5
verified fuzzy2
unresolved93
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 9b279bdf-f850-4297-90fd-6741b5310583 · outbound

This paper cites Fine-Tuning Language Models from Human Preferences.

RewardAnything: Generalizable Principle-Following Reward Models Fine-Tuning Language Models from Human Preferences

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.668341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56102ffb-be24-4cca-8208-538f0502a5a6 · outbound

This paper cites Training language models to follow instructions with human feedback,.

RewardAnything: Generalizable Principle-Following Reward Models Training language models to follow instructions with human feedback,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.674152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f72af75e-4930-41bf-851a-696ec6e5388e · outbound

This paper cites Deep reinforcement learning from human preferences,.

RewardAnything: Generalizable Principle-Following Reward Models Deep reinforcement learning from human preferences,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.679780Z

Source-reported events for the cited work

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Observation 85a9511f-355b-47bb-aee9-2d38ad65ee30 · outbound

This paper cites Learning to summarize with human feedback,.

RewardAnything: Generalizable Principle-Following Reward Models Learning to summarize with human feedback,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.684775Z

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Observation 9312a7b4-5134-49da-aaa8-dd7b93a8885d · outbound

This paper cites A General Language Assistant as a Laboratory for Alignment.

RewardAnything: Generalizable Principle-Following Reward Models A General Language Assistant as a Laboratory for Alignment

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.689596Z

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Observation 882b93d0-697f-4cf3-b839-d141d3bdef4e · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

RewardAnything: Generalizable Principle-Following Reward Models Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.694852Z

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Observation 6f079aa2-70ae-4aeb-8524-49e57e6fd4b7 · outbound

This paper cites RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style.

RewardAnything: Generalizable Principle-Following Reward Models RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.700804Z

Source-reported events for the cited work

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Observation 4eab97cf-aac4-410b-a980-cb5daf48fca0 · outbound

This paper cites A survey of reinforcement learning from human feedback,.

RewardAnything: Generalizable Principle-Following Reward Models A survey of reinforcement learning from human feedback,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.705995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba520f4b-043f-456e-a640-7cc474ca65e9 · outbound

This paper cites Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs.

RewardAnything: Generalizable Principle-Following Reward Models Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.710698Z

Source-reported events for the cited work

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Observation ddcae839-bad5-4a40-90aa-3b24b2270704 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena,.

RewardAnything: Generalizable Principle-Following Reward Models Judging llm-as-a-judge with mt-bench and chatbot arena,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.715361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e02376c-6f72-43ad-90fd-b9cf08e846b3 · outbound

This paper cites Evaluating Large Language Models at Evaluating Instruction Following.

RewardAnything: Generalizable Principle-Following Reward Models Evaluating Large Language Models at Evaluating Instruction Following

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.720772Z

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Observation 647a9c0a-a442-4d10-ba1b-77b7e8c222b5 · outbound

This paper cites KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models.

RewardAnything: Generalizable Principle-Following Reward Models KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.725726Z

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Observation 5e54f08c-bccc-4bfa-94f7-a66e374e2086 · outbound

This paper cites Helpsteer2: Open-source dataset for training top-performing reward models,.

RewardAnything: Generalizable Principle-Following Reward Models Helpsteer2: Open-source dataset for training top-performing reward models,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.731714Z

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Observation 31155be0-5e2c-4c8f-baf2-db78c69e4cb1 · outbound

This paper cites Alpacafarm: A simulation framework for methods that learn from human feedback,.

RewardAnything: Generalizable Principle-Following Reward Models Alpacafarm: A simulation framework for methods that learn from human feedback,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.736332Z

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Observation 95a650e7-7f8c-4641-ae0c-28e9bd3de960 · outbound

This paper cites Let’s verify step by step,.

RewardAnything: Generalizable Principle-Following Reward Models Let’s verify step by step,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.741111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a61d097b-10a7-4888-b9bb-c5df544d0818 · outbound

This paper cites Rank analysis of incomplete block designs: I. the method of paired comparisons,.

RewardAnything: Generalizable Principle-Following Reward Models Rank analysis of incomplete block designs: I. the method of paired comparisons,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.745739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 897d129d-c17a-4482-b52e-b6f713c33977 · outbound

This paper cites PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization.

RewardAnything: Generalizable Principle-Following Reward Models PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.750627Z

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Observation 7b388b80-12ab-40f5-bf29-edf188dc48eb · outbound

This paper cites Prometheus: Inducing fine-grained evaluation capability in language models,.

RewardAnything: Generalizable Principle-Following Reward Models Prometheus: Inducing fine-grained evaluation capability in language models,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.755519Z

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Observation 35da3c3b-5ae6-42f6-ae7e-6063175a4487 · outbound

This paper cites Understanding dataset difficulty with v-usable information,.

RewardAnything: Generalizable Principle-Following Reward Models Understanding dataset difficulty with v-usable information,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.760307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3b495a0-ac32-4338-a9eb-9404eed09896 · outbound

This paper cites RewardBench: Evaluating Reward Models for Language Modeling.

RewardAnything: Generalizable Principle-Following Reward Models RewardBench: Evaluating Reward Models for Language Modeling

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.764837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 284e87a8-4613-42db-9312-a2f323338b5d · outbound

This paper cites How to Evaluate Reward Models for RLHF.

RewardAnything: Generalizable Principle-Following Reward Models How to Evaluate Reward Models for RLHF

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.769759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e30463e9-60e3-492e-8b33-8d61471e0f3a · outbound

This paper cites MPO: An Efficient Post-Processing Framework for Mixing Diverse Preference Alignment.

RewardAnything: Generalizable Principle-Following Reward Models MPO: An Efficient Post-Processing Framework for Mixing Diverse Preference Alignment

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.774840Z

Source-reported events for the cited work

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Observation 1cbe3cfc-5f4c-4f0c-b08c-fceb2f81098c · outbound

This paper cites SALMON: Self-Alignment with Instructable Reward Models.

RewardAnything: Generalizable Principle-Following Reward Models SALMON: Self-Alignment with Instructable Reward Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.780144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06bf0455-4fe3-404f-9c02-5543eb4f7fd5 · outbound

This paper cites Inference-time scaling for generalist reward modeling,.

RewardAnything: Generalizable Principle-Following Reward Models Inference-time scaling for generalist reward modeling,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.785130Z

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Observation 6846a318-8e26-4a35-a5f3-c3e54524ba47 · outbound

This paper cites Rm-r1: Reward modeling as reasoning,.

RewardAnything: Generalizable Principle-Following Reward Models Rm-r1: Reward modeling as reasoning,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.789757Z

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Observation 385a7958-a8a7-4e24-8094-3a29b2cc48a5 · outbound

This paper cites Agentic Reward Modeling: Integrating Human Preferences with Verifiable Correctness Signals for Reliable Reward Systems.

RewardAnything: Generalizable Principle-Following Reward Models Agentic Reward Modeling: Integrating Human Preferences with Verifiable Correctness Signals for Reliable Reward Systems

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.794352Z

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Observation 7c913abf-9885-4020-8c26-9ea29f1c011b · outbound

This paper cites Proximal Policy Optimization Algorithms.

RewardAnything: Generalizable Principle-Following Reward Models Proximal Policy Optimization Algorithms

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.799281Z

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Observation 585bd097-d7a4-4279-a548-f10e14c4d904 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

RewardAnything: Generalizable Principle-Following Reward Models DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.804029Z

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Observation 5e9c6673-1a60-41f9-af83-2bb8bfbdd2bc · outbound

This paper cites What makes a reward model a good teacher? an optimization perspective,.

RewardAnything: Generalizable Principle-Following Reward Models What makes a reward model a good teacher? an optimization perspective,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.808458Z

Source-reported events for the cited work

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Observation 4de5ecbc-5476-40f2-ae93-403b085cc640 · outbound

This paper cites Rethinking Reward Model Evaluation Through the Lens of Reward Overoptimization.

RewardAnything: Generalizable Principle-Following Reward Models Rethinking Reward Model Evaluation Through the Lens of Reward Overoptimization

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:04:08.243595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a27bbd21-8242-4092-b6a8-dd026c14e7e7 · outbound

This paper cites OffsetBias: Leveraging Debiased Data for Tuning Evaluators.

RewardAnything: Generalizable Principle-Following Reward Models OffsetBias: Leveraging Debiased Data for Tuning Evaluators

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.819184Z

Source-reported events for the cited work

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Observation eed00d0e-6f1d-4820-a85f-fc1adb3eada8 · outbound

This paper cites PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference.

RewardAnything: Generalizable Principle-Following Reward Models PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.823967Z

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Observation ff7853db-ca4b-4728-a698-24b306bfa837 · outbound

This paper cites XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models.

RewardAnything: Generalizable Principle-Following Reward Models XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.829011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3caeb390-76d5-4292-abbc-7d1102c841bb · outbound

This paper cites Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking.

RewardAnything: Generalizable Principle-Following Reward Models Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.833906Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7c2a000-22cd-47a4-8c5e-76e767e818af · outbound

This paper cites Transforming and Combining Rewards for Aligning Large Language Models.

RewardAnything: Generalizable Principle-Following Reward Models Transforming and Combining Rewards for Aligning Large Language Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.839256Z

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Observation 1f4e3b2f-1766-4840-8825-57e7aacc2330 · outbound

This paper cites Heimdall: test-time scaling on the generative verification.

RewardAnything: Generalizable Principle-Following Reward Models Heimdall: test-time scaling on the generative verification

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.844166Z

Source-reported events for the cited work

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Observation a9c81cf0-383d-47b2-8810-b36116b58808 · outbound

This paper cites When to solve, when to verify: Compute-optimal problem solving and generative verification for llm reasoning,.

RewardAnything: Generalizable Principle-Following Reward Models When to solve, when to verify: Compute-optimal problem solving and generative verification for llm reasoning,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.849638Z

Source-reported events for the cited work

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Observation d4655333-ac14-4745-9507-ef5406937add · outbound

This paper cites Large Language Models are Better Reasoners with Self-Verification.

RewardAnything: Generalizable Principle-Following Reward Models Large Language Models are Better Reasoners with Self-Verification

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.854412Z

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Observation 20a05919-d3b7-4492-afd8-954af94a293b · outbound

This paper cites GenPRM: Scaling Test-Time Compute of Process Reward Models via Generative Reasoning.

RewardAnything: Generalizable Principle-Following Reward Models GenPRM: Scaling Test-Time Compute of Process Reward Models via Generative Reasoning

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.860706Z

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Observation 67a8321e-6415-4aa3-bdc3-7eefe6eaae70 · outbound

This paper cites Dynamic Multi-Reward Weighting for Multi-Style Controllable Generation.

RewardAnything: Generalizable Principle-Following Reward Models Dynamic Multi-Reward Weighting for Multi-Style Controllable Generation

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.866772Z

Source-reported events for the cited work

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Observation 036900c6-a90b-497d-a489-085704e09552 · outbound

This paper cites Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment.

RewardAnything: Generalizable Principle-Following Reward Models Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.872561Z

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Observation 3838075c-3529-4139-a0c1-84cdfd69ff06 · outbound

This paper cites Meta-reward-net: Implicitly differentiable reward learning for preference-based reinforcement learning,.

RewardAnything: Generalizable Principle-Following Reward Models Meta-reward-net: Implicitly differentiable reward learning for preference-based reinforcement learning,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.878071Z

Source-reported events for the cited work

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Observation d1464911-8e0c-4a5c-9e45-9069c5ea5cba · outbound

This paper cites Reasoning Through Execution: Unifying Process and Outcome Rewards for Code Generation.

RewardAnything: Generalizable Principle-Following Reward Models Reasoning Through Execution: Unifying Process and Outcome Rewards for Code Generation

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.882676Z

Source-reported events for the cited work

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Observation 85489b8f-af8a-4d90-a8ff-eac910f95769 · outbound

This paper cites An Empirical Analysis of Uncertainty in Large Language Model Evaluations.

RewardAnything: Generalizable Principle-Following Reward Models An Empirical Analysis of Uncertainty in Large Language Model Evaluations

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.887470Z

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Observation 9a17a9eb-7e34-45b2-871d-73a3db0b36cf · outbound

This paper cites Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models.

RewardAnything: Generalizable Principle-Following Reward Models Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.892322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c3df9b4-277a-4135-b4c4-75ffb9785030 · outbound

This paper cites Reward Shaping to Mitigate Reward Hacking in RLHF.

RewardAnything: Generalizable Principle-Following Reward Models Reward Shaping to Mitigate Reward Hacking in RLHF

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.897214Z

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Observation 726b7ead-80f1-4df5-a9f2-480b78a979cd · outbound

This paper cites Reward Model Ensembles Help Mitigate Overoptimization.

RewardAnything: Generalizable Principle-Following Reward Models Reward Model Ensembles Help Mitigate Overoptimization

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.902238Z

Source-reported events for the cited work

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Observation 8b1f6274-a680-4173-82d8-64c2de1361b1 · outbound

This paper cites Language models are few-shot learners,.

RewardAnything: Generalizable Principle-Following Reward Models Language models are few-shot learners,

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.906934Z

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Observation fda1a968-0a31-4e89-a696-40536e1fd5b8 · outbound

This paper cites PaLM 2 Technical Report.

RewardAnything: Generalizable Principle-Following Reward Models PaLM 2 Technical Report

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.911443Z

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Observation 789a1e01-ae63-467d-9284-3fd6be070583 · outbound

This paper cites Gpt-4 technical report,.

RewardAnything: Generalizable Principle-Following Reward Models Gpt-4 technical report,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.916486Z

Source-reported events for the cited work

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Observation 4d096eb2-2766-4bb7-8d6f-d29ce7e81c16 · outbound

This paper cites A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity.

RewardAnything: Generalizable Principle-Following Reward Models A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.921103Z

Source-reported events for the cited work

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Observation 85a8a507-cc31-4994-bf67-281478cd8ce0 · outbound

This paper cites A fast learning algorithm for deep belief nets,.

RewardAnything: Generalizable Principle-Following Reward Models A fast learning algorithm for deep belief nets,

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.925730Z

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Observation 29c94fe2-a672-4bff-8d4d-b1458d710815 · outbound

This paper cites Goodfellow, Y.

RewardAnything: Generalizable Principle-Following Reward Models Goodfellow, Y

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.930247Z

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Observation 979e9e92-d7d7-4316-88ec-3ec571473c55 · outbound

This paper cites Self-Instruct: Aligning Language Models with Self-Generated Instructions.

RewardAnything: Generalizable Principle-Following Reward Models Self-Instruct: Aligning Language Models with Self-Generated Instructions

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.934924Z

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Observation 79f2caed-bde8-4a7a-88e4-8b78accb4f8e · outbound

This paper cites Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping.

RewardAnything: Generalizable Principle-Following Reward Models Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.940032Z

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Observation 1337053c-cbc1-4d73-99c8-b887f3c611c2 · outbound

This paper cites How to fine-tune bert for text classification?.

RewardAnything: Generalizable Principle-Following Reward Models How to fine-tune bert for text classification?

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.944747Z

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Observation f68efd9f-1b6f-4349-9070-c4fa21b6fbbc · outbound

This paper cites Natural language question answering: the view from here,.

RewardAnything: Generalizable Principle-Following Reward Models Natural language question answering: the view from here,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.949212Z

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Observation 16119788-8a5d-4f1a-a803-8ea0c2bdd1f5 · outbound

This paper cites Natural questions: a benchmark for question answering research,.

RewardAnything: Generalizable Principle-Following Reward Models Natural questions: a benchmark for question answering research,

Reference 58

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Observation a8d7d9a4-f9c8-494e-afb9-e36450842e50 · outbound

This paper cites Attention is all you need,.

RewardAnything: Generalizable Principle-Following Reward Models Attention is all you need,

Reference 59

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Observation 23240761-a0d3-47f9-8aaa-2ffa09540afd · outbound

This paper cites A Survey on Evaluation of Large Language Models.

RewardAnything: Generalizable Principle-Following Reward Models A Survey on Evaluation of Large Language Models

Reference 60

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Observation e48dfae5-2c14-485d-910a-44cfe1603345 · outbound

This paper cites O’Reilly Media, Inc.

RewardAnything: Generalizable Principle-Following Reward Models O’Reilly Media, Inc

Reference 61

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Observation 4bb07342-09f8-4b42-b70f-ebacad01aa41 · outbound

This paper cites Deep learning tuning playbook,.

RewardAnything: Generalizable Principle-Following Reward Models Deep learning tuning playbook,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.974250Z

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Observation 34600fe8-cbf0-4e47-bebe-81c353a602b5 · outbound

This paper cites Glue: A multi-task bench- mark and analysis platform for natural language understanding,.

RewardAnything: Generalizable Principle-Following Reward Models Glue: A multi-task bench- mark and analysis platform for natural language understanding,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.979209Z

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Observation 10379657-7d58-4459-81fd-de8febfd5de2 · outbound

This paper cites GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective.

RewardAnything: Generalizable Principle-Following Reward Models GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective

Reference 64

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Observation 8d338659-03da-4d26-88a0-ba50fec7d0f2 · outbound

This paper cites The Llama 3 Herd of Models.

RewardAnything: Generalizable Principle-Following Reward Models The Llama 3 Herd of Models

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.988674Z

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Observation 2afbe2cd-240a-4140-a316-0e57d8408983 · outbound

This paper cites Lora: Low-rank adaptation of large language models,.

RewardAnything: Generalizable Principle-Following Reward Models Lora: Low-rank adaptation of large language models,

Reference 66

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Observation 91c101b8-0f39-427b-a6c4-5a9c707b8781 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

RewardAnything: Generalizable Principle-Following Reward Models Training Verifiers to Solve Math Word Problems

Reference 67

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Observation 9171a74f-991e-4ff4-b18d-8ed9ca0db66c · outbound

This paper cites NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark.

RewardAnything: Generalizable Principle-Following Reward Models NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.003448Z

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Observation b62b8a95-8eb5-48b6-a927-22fa4dd3188b · outbound

This paper cites Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters,.

RewardAnything: Generalizable Principle-Following Reward Models Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters,

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.008038Z

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Observation a201190d-f322-4fac-8904-48bca019f5e7 · outbound

This paper cites Zero-infinity: Breaking the gpu memory wall for extreme scale deep learning,.

RewardAnything: Generalizable Principle-Following Reward Models Zero-infinity: Breaking the gpu memory wall for extreme scale deep learning,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.012476Z

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Observation 18585e90-09fc-4b26-8905-2e866c416456 · outbound

This paper cites Supervised Knowledge Makes Large Language Models Better In-context Learners.

RewardAnything: Generalizable Principle-Following Reward Models Supervised Knowledge Makes Large Language Models Better In-context Learners

Reference 71

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:04:07.758392Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8225683a-db5b-4222-a75b-5c71558b5fc6 · outbound

This paper cites Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity.

RewardAnything: Generalizable Principle-Following Reward Models Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.022136Z

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Observation 404cbede-a3c2-48d9-a35f-4fe3734b43e8 · outbound

This paper cites Large language models are zero- shot reasoners,.

RewardAnything: Generalizable Principle-Following Reward Models Large language models are zero- shot reasoners,

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.027152Z

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Observation 83ab8c1a-22ac-4a8f-9eac-3cb3de3b8b80 · outbound

This paper cites LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion.

RewardAnything: Generalizable Principle-Following Reward Models LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.031741Z

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Observation 9bcc8e31-95e2-4215-9e50-b123a60c7ee0 · outbound

This paper cites The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models.

RewardAnything: Generalizable Principle-Following Reward Models The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.036636Z

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Observation f446cdd1-8045-4f10-97d8-1fdee4fe94fa · outbound

This paper cites UltraFeedback: Boosting Language Models with Scaled AI Feedback.

RewardAnything: Generalizable Principle-Following Reward Models UltraFeedback: Boosting Language Models with Scaled AI Feedback

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.042216Z

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Observation 1afc5850-2586-4b53-af6a-4fe07349a318 · outbound

This paper cites Qwen2 Technical Report.

RewardAnything: Generalizable Principle-Following Reward Models Qwen2 Technical Report

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.051915Z

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Observation 32811580-272d-4435-afb6-65154165c245 · outbound

This paper cites Team, “Qwen3,” April 2025.

RewardAnything: Generalizable Principle-Following Reward Models Team, “Qwen3,” April 2025

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.056870Z

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Observation 7f7d287e-2326-4723-9c4c-36296bc5f83c · outbound

This paper cites HybridFlow: A Flexible and Efficient RLHF Framework.

RewardAnything: Generalizable Principle-Following Reward Models HybridFlow: A Flexible and Efficient RLHF Framework

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.061627Z

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Observation 2ed5251c-e61f-408c-89ff-b365ffdb9234 · outbound

This paper cites A framework for training large language models for code generation via proximal policy optimization,.

RewardAnything: Generalizable Principle-Following Reward Models A framework for training large language models for code generation via proximal policy optimization,

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.066384Z

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Observation 08592c85-be79-4986-bd43-1d4168896b0e · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

RewardAnything: Generalizable Principle-Following Reward Models Gemini: A Family of Highly Capable Multimodal Models

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.075157Z

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Observation 6c5e91f6-313d-488d-af17-339cb0313ca2 · outbound

This paper cites Generative Reward Models.

RewardAnything: Generalizable Principle-Following Reward Models Generative Reward Models

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.079919Z

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Observation db7ccc68-7dbe-4d98-b54a-14efed3aedd2 · outbound

This paper cites Improving context-aware preference modeling for language models,.

RewardAnything: Generalizable Principle-Following Reward Models Improving context-aware preference modeling for language models,

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.084937Z

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Observation 8a617fe4-8e3b-498f-b90c-1866807daa7d · outbound

This paper cites Defining and Detecting Vulnerability in Human Evaluation Guidelines: A Preliminary Study Towards Reliable NLG Evaluation.

RewardAnything: Generalizable Principle-Following Reward Models Defining and Detecting Vulnerability in Human Evaluation Guidelines: A Preliminary Study Towards Reliable NLG Evaluation

Reference 86

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:04:07.578996Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 87488f2b-c604-425b-8a52-626fdfa5666f · outbound

This paper cites Best practices for the human evaluation of automatically generated text,.

RewardAnything: Generalizable Principle-Following Reward Models Best practices for the human evaluation of automatically generated text,

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.094686Z

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Observation fa1a059a-bdf8-4893-9288-213356b36821 · outbound

This paper cites DeepSeek-V3 Technical Report.

RewardAnything: Generalizable Principle-Following Reward Models DeepSeek-V3 Technical Report

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.099307Z

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Observation 4be1c353-9fea-41a9-b0d1-faf73251904c · outbound

This paper cites Leveraging Large Language Models for NLG Evaluation: Advances and Challenges.

RewardAnything: Generalizable Principle-Following Reward Models Leveraging Large Language Models for NLG Evaluation: Advances and Challenges

Reference 89

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:04:07.539350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation aac12416-9c89-4f07-a085-38a7e4404567 · outbound

This paper cites FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models.

RewardAnything: Generalizable Principle-Following Reward Models FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.109652Z

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Observation 27f6e241-7a3b-4db7-86b9-4d656f555e4c · outbound

This paper cites From generation to judgment: Opportunities and challenges of llm-as-a-judge,.

RewardAnything: Generalizable Principle-Following Reward Models From generation to judgment: Opportunities and challenges of llm-as-a-judge,

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.114445Z

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Observation 77f5f3d2-ef74-4f68-a451-c39f90ca04b3 · outbound

This paper cites Automating Dataset Updates Towards Reliable and Timely Evaluation of Large Language Models.

RewardAnything: Generalizable Principle-Following Reward Models Automating Dataset Updates Towards Reliable and Timely Evaluation of Large Language Models

Reference 92

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Observation 3787a9e0-2fa5-4f7c-a854-d2cd1140ca1d · outbound

This paper cites A Comprehensive Survey of Contamination Detection Methods in Large Language Models.

RewardAnything: Generalizable Principle-Following Reward Models A Comprehensive Survey of Contamination Detection Methods in Large Language Models

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.124211Z

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Observation 1a4e773d-0972-41b0-b0c5-f77d719df2f4 · outbound

This paper cites Prompt-to-Leaderboard.

RewardAnything: Generalizable Principle-Following Reward Models Prompt-to-Leaderboard

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.129328Z

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Observation 16fb42d0-8d71-4213-994e-65da12dda5f6 · outbound

This paper cites Scaling laws for reward model overoptimization,.

RewardAnything: Generalizable Principle-Following Reward Models Scaling laws for reward model overoptimization,

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.135219Z

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Observation a978274c-3e04-4b41-9561-e696d770c2d0 · outbound

This paper cites Self-Generated Critiques Boost Reward Modeling for Language Models.

RewardAnything: Generalizable Principle-Following Reward Models Self-Generated Critiques Boost Reward Modeling for Language Models

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.139918Z

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Observation e2782c99-9211-4367-ad11-4a63a5f59a4f · outbound

This paper cites Critique-out-Loud Reward Models.

RewardAnything: Generalizable Principle-Following Reward Models Critique-out-Loud Reward Models

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.144776Z

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Observation 7f3f9ad9-0e3f-4bcf-b043-4f51188e8a69 · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

RewardAnything: Generalizable Principle-Following Reward Models Constitutional AI: Harmlessness from AI Feedback

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.149524Z

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Observation bedcc321-eb0c-4115-b9dd-2143ccb00f25 · outbound

This paper cites Is Elo Rating Reliable? A Study Under Model Misspecification.

RewardAnything: Generalizable Principle-Following Reward Models Is Elo Rating Reliable? A Study Under Model Misspecification

Reference 99

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:04:07.278235Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d3eb81df-c2b1-4ff0-bf88-6ac5df644d5b · outbound

This paper cites On the biology of a large language model,.

RewardAnything: Generalizable Principle-Following Reward Models On the biology of a large language model,

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:07.159228Z

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Observation 2ea09ea2-b1c6-4bdd-a03b-49a91e102ab8 · outbound

This paper cites Gpt-4.1 and gpt-4.1 nano overview,.

RewardAnything: Generalizable Principle-Following Reward Models Gpt-4.1 and gpt-4.1 nano overview,

Reference 101

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3ec7d72a-c526-44ee-a919-72b4c4096480 · outbound

This paper cites Gemini 2.5: Our most intelligent ai model,.

RewardAnything: Generalizable Principle-Following Reward Models Gemini 2.5: Our most intelligent ai model,

Reference 102

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:08.962777Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation b69cfabc-835e-44b2-8e20-58f323790bed · inbound

Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle cites this paper.

Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle RewardAnything: Generalizable Principle-Following Reward Models

Reference 231

Resolution: unresolved; no resolver link, observed 2026-08-04T16:07:46.427272Z

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Observation f72b0cf3-5632-41cc-82c9-10b554b62a62 · inbound

RLBFF: Binary Flexible Feedback to bridge between Human Feedback & Verifiable Rewards cites this paper.

RLBFF: Binary Flexible Feedback to bridge between Human Feedback & Verifiable Rewards RewardAnything: Generalizable Principle-Following Reward Models

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-21T22:10:42.036446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4dbe7bcd-ddd2-4f60-b0a1-8deb8beb1af6 · inbound

Beyond Verifiable Rewards: Rubric-Based GRM for Reinforced Fine-Tuning SWE Agents cites this paper.

Beyond Verifiable Rewards: Rubric-Based GRM for Reinforced Fine-Tuning SWE Agents RewardAnything: Generalizable Principle-Following Reward Models

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2db5892d-8c19-45ac-9fec-1ef606dfc1dc · inbound

Beyond Overlap Metrics: Rewarding Reasoning and Preferences for Faithful Multi-Role Dialogue Summarization cites this paper.

Beyond Overlap Metrics: Rewarding Reasoning and Preferences for Faithful Multi-Role Dialogue Summarization RewardAnything: Generalizable Principle-Following Reward Models

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:51:45.655359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3c8adfd8-f24e-475f-b765-777ab48bdc7f · inbound

DoGMaTiQ: Automated Generation of Question-and-Answer Nuggets for Report Evaluation cites this paper.

DoGMaTiQ: Automated Generation of Question-and-Answer Nuggets for Report Evaluation RewardAnything: Generalizable Principle-Following Reward Models

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:31:07.401146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6aac035e-19a1-41be-b03e-d3bcaed68a42 · inbound

DoGMaTiQ: Automated Generation of Question-and-Answer Nuggets for Report Evaluation cites this paper.

DoGMaTiQ: Automated Generation of Question-and-Answer Nuggets for Report Evaluation RewardAnything: Generalizable Principle-Following Reward Models

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-07-01T00:15:09.597128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 47d74668-400a-4c4f-97b1-af72d8570a03 · inbound

Skill-RM: Unifying Heterogeneous Evaluation Criteria via Agent Skill cites this paper.

Skill-RM: Unifying Heterogeneous Evaluation Criteria via Agent Skill RewardAnything: Generalizable Principle-Following Reward Models

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T11:02:01.384983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4d9bc8bb-3426-4345-92d4-2e42081b3aeb · inbound

Support Vector Rubrics: Closing the Gap Between Self-Generated and Human Rubrics cites this paper.

Support Vector Rubrics: Closing the Gap Between Self-Generated and Human Rubrics RewardAnything: Generalizable Principle-Following Reward Models

Reference 42

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T20:47:23.335587Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.